Online distributed price-based control of DR resources with competitive guarantees

Siyuan Xu, Liren Yu, Xiaojun Lin, Jin Dong, Yaosuo Xue

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Demand response (DR) of building HVAC load can provide crucial demand-side flexibility for the future smart grid. Compared to direct load control, price-based control can respect the customers' autonomy and privacy. However, it is challenging for price-based control to attain provable performance guarantees under future uncertainty. In this paper, we propose a framework for a utility to perform price-based control of flexible building load within the utility's service area, in order to attain competitive performance guarantees in terms of controlling the system peak demand under future uncertainty. By adopting a two-step approach, our online price-based control solution can attain a provable competitive ratio for all possible realizations within a given uncertainty set. Simulation experiments demonstrate that, with a robustification procedure, our solution can perform well not only for worst-case inputs, but also for average-case inputs.

Original languageEnglish
Title of host publicatione-Energy 2022 - Proceedings of the 2022 13th ACM International Conference on Future Energy Systems
PublisherAssociation for Computing Machinery, Inc
Pages17-33
Number of pages17
ISBN (Electronic)9781450393973
DOIs
StatePublished - Jun 28 2022
Event13th ACM International Conference on Future Energy Systems, e-Energy 2022 - Virtual, Online, United States
Duration: Jun 28 2022Jul 1 2022

Publication series

Namee-Energy 2022 - Proceedings of the 2022 13th ACM International Conference on Future Energy Systems

Conference

Conference13th ACM International Conference on Future Energy Systems, e-Energy 2022
Country/TerritoryUnited States
CityVirtual, Online
Period06/28/2207/1/22

Funding

This work has been partially supported by U.S. Department of Energy, Office of Electricity, with grant DE-OE0000921 and under contract DE-AC05-00OR22725, and by NSF through grant ECCS-2129631.

FundersFunder number
National Science FoundationECCS-2129631
U.S. Department of Energy
Office of ElectricityDE-AC05-00OR22725, DE-OE0000921

    Keywords

    • Competitive online algorithm
    • Demand response
    • Peak demand
    • Price-based control

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